class_docstr: Computes squared pearson correlation (r^2) metric.
class_docstr_tok: ["Computes","squared","pearson","correlation","(","r^2",")","metric","."]
code: def merge_accumulators(
      self,
      accumulators: Iterable[_SquaredPearsonCorrelationAccumulator],
  ) -> _SquaredPearsonCorrelationAccumulator:
      accumulators = iter(accumulators)
      result = next(accumulators)
      for accumulator in accumulators:
          result.total_weighted_labels += (
              accumulator.total_weighted_labels
          )
          result.total_weighted_predictions += (
              accumulator.total_weighted_predictions
          )
          result.total_weighted_squared_labels += (
              accumulator.total_weighted_squared_labels
          )
          result.total_weighted_squared_predictions += (
              accumulator.total_weighted_squared_predictions
          )
          result.total_weighted_labels_times_predictions += (
              accumulator.total_weighted_labels_times_predictions
          )
          result.total_weighted_examples += (
              accumulator.total_weighted_examples
          )
      return result
  
file_path: model-analysis/tensorflow_model_analysis/metrics/squared_pearson_correlation.py
imports: from typing import Dict, Iterable, Optional
  import apache_beam
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 4 for calling the _SquaredPearsonCorrelationCombiner obj's underlying member method code with example usage: obj.merge_accumulators(accumulators) and returns: result